Accumulo and the Convergence of Machine Learning, Big Data, and Supercomputing

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Use Case


Machine learning, big data, and simulation challenges have led to a proliferation of computing hardware and software solutions. Hyperscale data centers, accelerators, and programmable logic can deliver enormous performance via a wide range of analytic environments and data storage technologies. Apache Accumulo is a unique technology with the potential to enable all of these fields. Effectively exploiting Accumulo in these fields requires mathematically rigorous interfaces that allow users to focus on their domains. Mathematically rigorous interfaces are at the core MIT Lincoln Laboratory Supercomputing Center (LLSC) and enable the LLSC to deliver Apache Accumulo o thousands of scientists and engineers. This talk discusses the rapidly evolving computing landscape and how mathematically rigorous interfaces are the key to exploiting Apache Accumulo's advanced capabilities.


Jeremy Kepner
Fellow, MIT
Dr. Jeremy Kepner is a MIT Lincoln Laboratory Fellow. He founded the Lincoln Laboratory Supercomputing Center and pioneered the establishment of the Massachusetts Green High Performance Computing Center. He has developed novel big data and parallel computing software used by thousands of scientists and engineers worldwide. He has led several embedded computing efforts, which earned him a 2011 R&D 100 Award. Dr. Kepner has chaired SIAM Data Mining, the IEEE Big Data conference, and the IEEE High Performance Extreme Computing conference. Dr. Kepner is the author of two bestselling books, Parallel MATLAB and Graph Algorithms in the Language of Linear Algebra. His peer-reviewed publications include works on abstract algebra, astronomy, cloud computing, cybersecurity, data mining, databases, graph algorithms, health sciences, signal processing, and visualization. Dr. Kepner holds a BA degree in astrophysics from Pomona College and a PhD degree in astrophysics from Princeton University.